Adaptive selection of digital ECG filter

ABSTRACT

A method and system for filtering a detected ECG signal are disclosed. In a first aspect, the method comprises filtering the detected ECG signal using a plurality of digital filters. The method includes adaptively selecting one of the plurality of digital filters to maintain a minimum signal-to-noise ratio (SNR). In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

FIELD OF THE INVENTION

The present invention relates to sensors, and more particularly, to asensor device utilized to measure ECG signals using adaptive selectionof digital filters.

BACKGROUND

A sensor device can be placed on the upper-body of a user (e.g. chestarea) to sense an analog, single-lead, bipolar electrocardiogram (ECG)signal through electrodes that are attached to the skin of the user. Theanalog ECG signal is sampled and converted to the digital domain usingan analog-to-digital converter (ADC) and is passed to a signalprocessing unit of the sensor device to extract R wave to R waveintervals (RR intervals) and other related features of the ECG signal.

Typically, several ambient noises such as motion artifacts and baselinewander, caused by the movement of the user, are mixed with the ECGsignal and thus picked up by the sensor device resulting in lessaccurate ECG signal detection. Conventional methods of filteringdetected ECG signals include filtering the ECG signal using a fixedanalog anti-aliasing filter before the ECG signal is converted to thedigital domain by an ADC and then filtering the ECG signal using adigital band-pass filter that removes the baseline wander and the out ofthe band noise.

However, these conventional methods do not adequately filter ECG signalswith changing parameters. Therefore, there is a strong need for acost-effective solution that overcomes the above issue. The presentinvention addresses such a need.

SUMMARY OF THE INVENTION

A method and system for filtering a detected ECG signal are disclosed.In a first aspect, the method comprises filtering the detected ECGsignal using a plurality of digital filters. The method includesadaptively selecting one of the plurality of digital filters to maintaina minimum signal-to-noise ratio (SNR).

In a second aspect, the system comprises a wireless sensor devicecoupled to a user via at least one electrode, wherein the wirelesssensor device includes a processor and a memory device coupled to theprocessor, wherein the memory device stores an application which, whenexecuted by the processor, causes the processor to filter the detectedECG signal using a plurality of digital filters. The system furthercauses the processor to adaptively select one of the plurality ofdigital filters to maintain a minimum signal-to-noise ratio (SNR).

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures illustrate several embodiments of the inventionand, together with the description, serve to explain the principles ofthe invention. One of ordinary skill in the art will recognize that theembodiments illustrated in the figures are merely exemplary, and are notintended to limit the scope of the present invention.

FIG. 1 illustrates a wireless sensor device in accordance with anembodiment.

FIG. 2 illustrates a block diagram of adaptive filter selection inaccordance with an embodiment.

FIG. 3 illustrates a diagram of Kurtosis of an ECG signal in accordancewith an embodiment.

FIG. 4 illustrates a diagram of computing mid-beat SNR in accordancewith an embodiment.

FIG. 5 illustrates a flowchart of computer mid-beat SNR in accordancewith an embodiment.

FIG. 6 illustrates a flowchart of a high level overview of the QualityMetric calculation in accordance with an embodiment.

FIG. 7 illustrates a flowchart of a more detailed Quality Metriccalculation in accordance with an embodiment.

FIG. 8 illustrates a flowchart of computing an activity level inaccordance with an embodiment.

FIG. 9 illustrates a flowchart of adaptive selection of an ECG digitalfilter in accordance with an embodiment.

FIG. 10 illustrates a table of user notifications in accordance with anembodiment.

FIG. 11 illustrates a diagram comparing ECG signal quality using a fixedfilter and using adaptive selection in accordance with an embodiment.

FIG. 12 illustrates a method for filtering a detected ECG signal inaccordance with an embodiment.

DETAILED DESCRIPTION

The present invention relates to sensors, and more particularly, to asensor device utilized to measure ECG signals using adaptive selectionof digital filters. The following description is presented to enable oneof ordinary skill in the art to make and use the invention and isprovided in the context of a patent application and its requirements.Various modifications to the preferred embodiment and the genericprinciples and features described herein will be readily apparent tothose skilled in the art. Thus, the present invention is not intended tobe limited to the embodiments shown but is to be accorded the widestscope consistent with the principles and features described herein.

Utilizing a combination of adaptive filters, a sensor device moreaccurately detects the ECG signal of a user over conventional fixedfilter methodologies. A method and system in accordance with the presentinvention filters a detected ECG signal using a predetermined number ofparallel digital band-pass filters (e.g. 4) with varying 3 dB high-passcutoff frequencies (e.g. 1, 5, 10, and 20 Hz). By adaptively changingthe digital filter whose output is used for RR interval (or otherrelated features) calculation, a minimum signal-to-noise ratio (SNR) forthe ECG signal is maintained.

During sensing, the sensor device does not have a reference ECG signalavailable to conventionally measure noise and the SNR of the ECG signal.Thus, the sensor device utilizes a Quality Metric (QM) for each of thepredetermined number of parallel digital band-pass filters to estimatethe SNR of the ECG signal. Because calculation of a Quality Metricresults in power consumption by a microprocessor of the sensor device,the Quality Metric at each digital filter output can be calculated oneat a time. Additionally, the sensor device utilizes activity level dataor a level of user motion registered on a MEMS device embedded withinthe sensor device to measure noise and ECG signal quality. The measuredQM and activity level data are both utilized by the sensor device ascriteria for adaptive selection and changing of the digital filter.

The method and system in accordance with the present invention ensuresthat frequent switching between different digital filters is eliminated.Frequent switching is undesirable because every time a filter isswitched, there is a settling time and lag that affects the continuousand accurate measurement of the ECG signal. By determining whether aminimum ECG Quality Metric is maintained, the sensor device ensures thata digital filter is not changed even though the Quality Metric output ofone or more of the other digital filters is higher. The resultinghysteresis ensures stability in the selection of the digital filter andprevents erratic switching between digital filters based on transientand short bursts of noise.

One of ordinary skill in the art readily recognizes that a variety ofsensor devices can be utilized to measure ECG signals using adaptiveselection of digital filters including portable wireless sensor deviceswith embedded circuitry in a patch form factor and that would be withinthe spirit and scope of the present invention.

To describe the features of the present invention in more detail, refernow to the following description in conjunction with the accompanyingFigures.

FIG. 1 illustrates a wireless sensor device 100 in accordance with anembodiment. The wireless sensor device 100 includes a sensor 102, aprocessor 104 coupled to the sensor 102, a memory 106 coupled to theprocessor 104, an application 108 coupled to the memory 106, and atransmitter 110 coupled to the application 108. The sensor 102 obtainsdata from the user and transmits the data to the memory 106 and in turnto the application 108. The processor 104 executes the application 108to process ECG signal information of the user. The information istransmitted to the transmitter 110 and in turn relayed to another useror device.

In one embodiment, the sensor 102 comprises two electrodes to measurecardiac activity and a MEMS device (e.g. accelerometer) to recordphysical activity levels and the processor 104 comprises amicroprocessor. One of ordinary skill in the art readily recognizes thata variety of devices can be utilized for the processor 104, the memory106, the application 108, and the transmitter 110 and that would bewithin the spirit and scope of the present invention.

FIG. 2 illustrates a block diagram 200 of adaptive filter selection inaccordance with an embodiment. The block diagram 200 includes ananalog-to-digital converter A/D 202, four digital filters 204 coupled tothe A/D 202, a quality metric calculation unit 206 coupled to the fourdigital filters 204, to a filter multiplexer (MUX) 208, and to a R-Rinterval calculation algorithm unit 210. One of ordinary skill in theart readily recognizes that different number of digital filters can becoupled to the A/D including but not limited to 4, 6, and 10 filters andthat would be within the spirit and scope of the present invention. Theblock diagram 200 also includes a MEMS device 212 coupled to an activitymeasurement unit 214, wherein the activity measurement unit 214 iscoupled to the filter MUX 208.

In FIG. 2, ECG signals are detected by the sensor 102 of the wirelesssensor device 100 and transmitted to the A/D 202. Additionally, in FIG.2, physical movements of the user are detected by the MEMS device 212.After receiving the ECG signals and converting them to the digitaldomain, the A/D 202 transmits the signals through the four digitalfilters 204 and to the quality metric calculation unit 206 whichcalculates a Quality Metric for each filter individually to preserveprocessing power.

The filter MUX 208 receives each of these calculated Quality Metricvalues which aids in the selection of which digital filter to use forthe R-R interval calculation by the R-R interval calculation algorithmunit 210. After detecting the physical movements of the user, the MEMSdevice 204 transmits the data to the activity measurement unit 214 tocalculate activity levels and to transmit the calculated activity levelsto the filter MUX 208 which also aids in the selection of which digitalfilter to use for the R-R interval calculation by the R-R intervalcalculation algorithm unit 210.

In one embodiment, calculation of the Quality Metric includes both astatistical quality indicator component and a mid-beat signal-to-noiseratio (SNR) quality indicator component. As a result of a lack of areference ECG signal available during the time of sensing, statisticalproperties and parameters of the motion artifacts, background noise, andthe ECG signal are utilized to assess the quality of the ECG signal. Forstatistical parameters to accurately capture the quality of the ECGsignal, a large number of data samples is required. Therefore, thestatistical parameters are typically not sensitive to faster changes insignal quality.

Using only statistical parameters as a signal quality indicator has thedrawback of having a low sensitivity to small noise power level.Therefore, the statistical quality indicator component is combined withthe mid-beat SNR quality indicator component. The mid-beat SNR qualityindicator component utilizes already detected QRS peaks of the ECGsignal to estimate the signal-to-noise ratio. Combining both thestatistical and mid-beat SNR quality indicator components results in aQuality Metric calculation that provides high sensitivity for differentlevels of noise.

An ECG signal has a sharp peak in the probability density function incontrast to background noise which has a flatter distribution. Thenoisier the ECG signal, the flatter the distribution of the combinationof ECG signal and noise. In one embodiment, a Kurtosis algorithm isutilized to measure sharp peaks of the distribution of a randomvariable. Kurtosis of a random variable (x) is defined asKurtosis(x)=(E(x−m)⁴)/(E((x−m)²)²), where E(x) is the expected value ofthe random variable x and m=E(x). Kurtosis of the ECG signal is a goodindicator of the level of noise corrupting the ECG signal. Therefore,calculation of the Kurtosis of the ECG signal represents the statisticalquality indicator component of the Quality Metric.

One of ordinary skill in the art readily recognizes that an ECG signalhas a high Kurtosis including but not limited to a value greater thanapproximately 10, a pure Gaussian signal has a Kurtosis including butnot limited to a value of approximately 3, and a motion artifact noisecorrupting the ECG signal has a Kurtosis including but not limited to avalue of approximately between 2 and 5, and that would be within thespirit and scope of the present invention.

FIG. 3 illustrates a diagram 300 of Kurtosis of an ECG signal inaccordance with an embodiment. In the diagram 300, the left FIG. 302shows an ECG signal corrupted by motion artifacts and the right FIG. 304shows Kurtosis of an ECG signal that decreases when combined with motionartifact and noise corruption.

Successive QRS peaks of the ECG signal are analyzed for the calculationof the mid-beat signal-to-noise ratio (SNR) quality indicator component.Part of the ECG signal is called the TP segment. The TP segment denotesthe area of the ECG signal that is between the end of a T wave of theprevious beat and the start of a P wave of the next beat. Under optimalconditions (e.g. very little to no noise corrupting the ECG signal), theTP segment is at a flat baseline. By computing the variance of the ECGsignal over a predetermined time period window in the middle of the TPsegment, an estimate of the noise power or amount of noise corruptingthe ECG signal is garnered.

To compute the mid-beat signal-to-noise ratio (SNR) quality indicatorcomponent, a ratio of Signal Power over Noise Power (mid-beat SNR=SignalPower/Noise Power) is calculated. Noise Power is calculated as avariance of the ECG signal over a predetermined time period window inthe middle of the TP segment is averaged over a plurality of beats.Signal Power is calculated as an average of the RS amplitude squaredover the plurality of beats. In one embodiment, a mid-point between twodetected R peaks of successive heartbeats is utilized for the mid-beatSNR quality indicator component calculation instead of detecting the Tand P waves to lower power consumption.

FIG. 4 illustrates a diagram 400 of computing mid-beat SNR in accordancewith an embodiment. In the diagram 400, the top FIG. 402 shows a cleanECG signal that includes a flat baseline TP segment and the bottom FIG.404 shows a noisy ECG signal that does not include a flat baseline TPsegment and instead includes a TP segment that has many fluctuations.

FIG. 5 illustrates a flowchart 500 of computing mid-beat SNR inaccordance with an embodiment. In the flowchart 500, an ECG signal isdetected by a wireless sensor device 100 and processed by a QRS peakdetection algorithm unit 502 which calculates QRS peak and mid-beat dataincluding but not limited to RS amplitude. The QRS peak and mid-beatdata is used to calculate the Signal Power via unit 504 and the NoisePower via unit 506. The Signal Power is calculated as the average of theRS amplitude squared over a predetermined number of beats. The NoisePower is calculated as the variance of the mid-beat predetermined timeperiod window over a predetermined number of beats. In one embodiment,the mid-beat predetermined time period window is 100 milliseconds andthe predetermined number of beats is 10 beats. The mid-beat SNR iscalculated as the ratio of Signal Power/Noise Power via unit 508.

The Quality Metric is calculated by combining the Kurtosis calculation(statistical quality indicator component) and the mid-beat SNRcalculation (mid-beat SNR quality indicator component). FIG. 6illustrates a flowchart 600 of a high level overview of the QualityMetric calculation in accordance with an embodiment. In the flowchart600, an ECG signal is detected by a wireless sensor device 100 andprocessed by both a QRS peak detection algorithm unit 602 and aStatistical Quality Metric calculation unit 604. The Statistical QualityMetric calculation unit 604 calculates a Kurtosis of the ECG signal. TheQRS peak detection algorithm unit 602 calculates QRS peak and mid-beatdata that is used to calculate the mid-beat SNR via the Mid-Beat SNRcalculation unit 606. The Quality Metric calculation unit 608 utilizesthe outputs of both the Statistical Quality Metric calculation unit 604and the Mid-Beat SNR calculation unit 606 to calculate the overallQuality Metric of the detected ECG signal.

FIG. 7 illustrates a flowchart 7000 of a more detailed Quality Metriccalculation in accordance with an embodiment. Referring to FIGS. 6 and 7together, after the Kurtosis of the ECG signal (SQM) is calculated viathe Statistical Quality Metric calculation unit 604, the SQM is comparedto a Threshold_SQM via 702. If SQM is greater than the Threshold_SQM,then SQM_Coeff=SQ1, but if SQM is not greater than the Threshold_SQM,then SQM_Coeff=SQ2. After the mid-beat SNR (MBSNR) is calculated via theMid-Beat SNR calculation unit 606, the MBSNR is compared to aThreshold_MBSNR via 704. If MBSNR is less than the Threshold_MBSNR, thenMBSNR_Coeff=MB1, but if MBSNR is not less than the Threshold_MBSNR, thenMBSNR_Coeff=MB2. The overall Quality Metric is calculated via unit 706per the following weighted linear combination equation: QualityMetric=SQM_Coeff*SQM+MBSNR_Coeff*MBSNR.

In FIG. 7, if SQM (which represents the Kurtosis of the ECG signal) isgreater than the Threshold_SQM, that typically indicates that thedetected ECG signal is of a higher quality. Therefore, the Kurtosiscalculation (statistical quality indicator component) is less sensitiveto small noise changes and so is weighted less in the overall QualityMetric calculation by setting SQ1<SQ2. The Kurtosis calculation has aregion of low sensitivity when the Kurtosis is a higher value (e.g.20-25).

Additionally, in FIG. 7, if MBSNR (which represents the mid-beat SNR ofthe ECG signal) is less than the Threshold_MBSNR, that typicallyindicates that the detected ECG signal is of a lower quality. Therefore,the mid-beat SNR calculation (mid-beat SNR quality indicator component)is less accurate when detecting beats and so is weighted less in theoverall Quality Metric calculation by setting MB1<MB2. The mid-beat SNRcalculation has a region of low sensitivity when the mid-beat SNR is alower value (e.g. below 5 dB).

FIG. 8 illustrates a flowchart 800 of computing an activity level inaccordance with an embodiment. In FIG. 8, the MEMS device 802 detectsactivity data in x, y, and z coordinates and passes the activity datathrough three parallel band pass filters 804. An absolute value of theactivity data is taken via 806 and the values are summed. The summedvalues are passed through a low-pass filter 808 which output theactivity level. In one embodiment, the parameters of the three parallelband pass filters 804 include but are not limited to a lowpass filterpole of 1 Hz and digital band pass filters with a denominatorcoefficient vector A=[1024, −992, 32], a numerator coefficient vectorB=[496, 0, −496], and a sampling rate fs=62.5 Hz.

FIG. 9 illustrates a flowchart 900 of adaptive selection of an ECGdigital filter in accordance with an embodiment. In the flowchart 900, adigital filter is selected based on an activity level that is detectedby a MEMS device of the wireless sensor device 100, via step 902. TheQuality Metric of the selected digital filter output is calculated overa predetermined time period (e.g. 30 seconds), via step 904. To preventfrequent switching and the lag time that ensues, a minimum ECG QualityMetric is maintained. Maintaining a minimum ECG Quality Metric ensuresstability in the selection of the digital filter so that although ahigher Quality Metric is available via another digital filter, anotherdigital filter is not selected to prevent erratic switching fromoccurring.

If the calculated Quality Metric is determined to be greater than QM_HI(which denotes a high quality ECG signal), via step 906, then theflowchart 900 analyzes whether the filter setting is at a lowest cutofffrequency setting of the utilized parallel digital filters, via step908. If yes (the filter setting is at the lowest cutoff frequencysetting), then the ECG signal is at a high quality and optimalprocessing level and so the flowchart 900 returns back to step 904. Ifno (the filter setting is not at the lowest cutoff frequency setting),then the Quality Metric of the previous filter setting output iscalculated over a predetermined time period (e.g. 30 seconds), via step910.

The calculated Quality Metric of the previous filter is compared to thethreshold QM_HI, via step 912. If the calculated Quality Metric of theprevious filter is greater than QM_HI, then the digital filter isswitched to the previous filter setting, via step 914, and the flowchart900 returns back to step 904. If the calculated Quality Metric of theprevious filter is not greater than QM_HI, the flowchart 900 returns tostep 904.

Referring back to step 906, if the calculated Quality Metric isdetermined to not be greater than QM_HI, the calculated Quality Metricis compared to QM_LO, via step 916. If the calculated Quality Metric isnot less than QM_LO, then it is determined to be between QM_HI and QM_LOand is thus an ECG signal with an average level of quality so there isno need to change the filter and the flowchart 900 returns back to step904.

If the calculated Quality Metric is less than QM_LO, then the flowchart900 analyzes whether the filter setting is at a highest cutoff frequencysetting of the utilized parallel digital filters, via step 918. If yes(the filter setting is at the highest cutoff frequency setting), thenuser alters are generated based on activity, Quality Metric, and ECGamplitude stating there are issues with the signal and/or connection. Ifno (the filter setting is not at the highest cutoff frequency setting),the next filter setting is selected and the flowchart 900 returns backto step 904 to calculate the Quality Metric of the selected next filtersetting.

In one embodiment, appropriate notifications are sent to a user of thewireless sensor device 100 in accordance with activity level, QualityMetric, and ECG amplitude calculations. FIG. 10 illustrates a table 1000of user notifications in accordance with an embodiment. In FIG. 10, ifthe Quality Metric, QRS amplitude, and activity level are all at a highlevel, then the diagnosis is a normal ECG signal and there is no action.If the Quality Metric and QRS amplitude are at a high level, and theactivity level is at a low level, then the diagnosis is a normal ECGsignal and there is no action.

If the Quality Metric and activity level are at a high level, and theQRS amplitude is at a low level, then the diagnosis is a weak but cleanECG signal and the action is to use a digital gain, which involvesmultiplication of the digital ECG signal by a factor greater than 1. Ifthe Quality Metric is at a high level, and the QRS amplitude and theactivity level are both at a low level, then the diagnosis is a weak butclean ECG signal and the action is to use a digital gain.

If the Quality Metric is at a low level, and the QRS amplitude and theactivity level are both at a high level, the diagnosis is a noisy ECGsignal due to motion artifact, bad skin contact, or wrong placement ofthe wireless sensor device 100 and the action is to warn the user orwait for the activity to become low and reassess. If the Quality Metricand the activity level are both at a low level, and the QRS amplitude isat a high level, or if the Quality Metric and the QRS amplitude are at alow level, and the activity level is at a high level, or if the QualityMetric, the QRS amplitude, and the activity level are all at a lowlevel, then the diagnosis is a noisy ECG signal due to bad contact orwrong placement and the action is to warn the user if the issuespersist.

FIG. 11 illustrates a diagram 1100 comparing ECG signal quality using afixed filter and using adaptive selection in accordance with anembodiment. In the diagram 1100, the quality metric of the fixed filterapproach significantly drops between the 200-300 seconds time periodwhereas the quality metric of the adaptive selection approach remainsrelatively stable at a quality metric value above 10 between the 200-300seconds time period.

FIG. 12 illustrates a method 1200 for filtering a detected ECG signal inaccordance with an embodiment. The method 1200 includes filtering thedetected ECG signal using a plurality of digital filters, via step 1202.The method 1200 includes adaptively selecting one of the plurality ofdigital filters to maintain a minimum signal-to-noise ratio (SNR), viastep 1204. In one embodiment, the plurality of digital filters includesa plurality of parallel digital band-pass filters with varying high-passcutoff frequencies (e.g. 1-20 Hz).

In one embodiment, the method 1200 further includes utilizing an outputof the adaptively selected digital filter to calculate features of thedetected ECG signal including but not limited to RR intervals. Themethod 1200 further includes calculating a Quality Metric for each ofthe plurality of digital filters, wherein the Quality Metric is utilizedto adaptively select one of the plurality of digital filters. In oneembodiment, the calculating is carried out on only one of the pluralityof digital filters at a time to conserve power consumption related tothe processing required for the calculating.

In one embodiment, the calculating of the Quality Metric comprisescalculating a statistical quality indicator (e.g. a Kurtosis calculationof the detected ECG signal) and calculating a mid-beat SNR qualityindicator (e.g. the ratio of the Signal Power/Noise Power) and thencombining these two quality indicators via a weighted linearcombination. The method 1200 further includes calculating an activitylevel using a microelectromechanical systems (MEMS) device that isembedded within the wireless sensor device 100 to measure noise of thedetected ECG signal.

In one embodiment, the method 1200 further includes maintaining aminimum Quality Metric to prevent erratic switching between theplurality of digital filters by comparing various high and low levelthresholds to the calculated Quality Metrics for each output of theplurality of digital filters. In one embodiment, the method 1200 furtherincludes providing notifications of ECG signal quality and recommendedactions for a user or operator of the wireless sensor device 100. Thenotifications are based upon the calculated Quality Metric, thecalculated activity level, and various other factors including but notlimited to QRS amplitude.

As above described, the method and system allow for filtering a detectedECG signal using adaptive selection of digital filters to maintain aminimum signal-to-noise ratio (SNR) and ECG signal quality. A wirelesssensor device detects an ECG signal which is then filtered using aplurality of dynamically adjusting digital filters. A Quality Metric iscalculated for each of the plurality of digital filters using both astatistical component and a mid-beat SNR component. The Quality Metricis used in combination with a detected activity level to adaptivelyselect one of the plurality of digital filters that maintains a minimumECG quality level thereby arriving at more accurate ECG signal basedcalculations.

A method and system for filtering a detected ECG signal has beendisclosed. Embodiments described herein can take the form of an entirelyhardware implementation, an entirely software implementation, or animplementation containing both hardware and software elements.Embodiments may be implemented in software, which includes, but is notlimited to, application software, firmware, resident software,microcode, etc.

The steps described herein may be implemented using any suitablecontroller or processor, and software application, which may be storedon any suitable storage location or computer-readable medium. Thesoftware application provides instructions that enable the processor tocause the receiver to perform the functions described herein.

Furthermore, embodiments may take the form of a computer program productaccessible from a computer-usable or computer-readable storage mediumproviding program code or program instructions for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable or computer-readablestorage medium can be any apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer-readable storage medium may be an electronic, magnetic,optical, electromagnetic, infrared, semiconductor system (or apparatusor device), or a propagation medium. Examples of a computer-readablestorage medium include a semiconductor or solid state memory, magnetictape, a removable computer diskette, a random access memory (RAM), aread-only memory (ROM), a rigid magnetic disk, and an optical disk.Current examples of optical disks include DVD, compact disk-read-onlymemory (CD-ROM), and compact disk-read/write (CD-R/W).

Although the present invention has been described in accordance with theembodiments shown, one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments and thosevariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method for filtering a detected ECG signalusing a plurality of digital filters, the method comprising: calculatinga kurtosis of a detected ECG signal as a statistical quality indicator;calculating a mid-beat signal-to-noise ratio (SNR) by analyzingsuccessive QRS peaks of the detected ECG signal; calculating a qualitymetric for a plurality of digital filters by combining the statisticalquality indicator and the mid-beat signal-to-noise ratio (SNR), whereinthe statistical quality indicator and the mid-beat SNR quality indicatorare combined via a weighted linear combination; and adaptively selectingone of the plurality of digital filters by comparing the calculatedquality metric to a minimum quality metric threshold and by determininga cutoff frequency setting of the one of the plurality of digitalfilters to prevent erratic switching between the plurality of digitalfilters.
 2. The method of claim 1, wherein the plurality of digitalfilters comprise a plurality of parallel digital band-pass filters withvarying high-pass cutoff frequencies.
 3. The method of claim 1, furthercomprising: utilizing an output of the adaptively selected digitalfilter to calculate an RR interval of the detected ECG signal.
 4. Themethod of claim 1, wherein the calculating the quality metric is only onone of the plurality of digital filters at a time to conserve powerconsumption.
 5. The method of claim 1, further comprising: calculatingan activity level using a microelectromechanical systems (MEMS) deviceembedded within a wireless sensor device to measure noise of thedetected ECG signal.
 6. The method of claim 5, further comprising:providing notifications of ECG signal quality and recommended actionsbased upon the calculated Quality Metric and the calculated activitylevel.
 7. The method of claim 1, wherein the calculating of the mid-beatSNR further comprises: calculating a ratio of signal power over noisepower.
 8. The method of claim 7, wherein the noise power is calculatedas a variance of the detected ECG signal over a predetermined timeperiod in a middle of a TP segment that is averaged over a plurality ofbeats.
 9. The method of claim 8, wherein the signal power is calculatedas an average of RS amplitude squared over the plurality of beats. 10.The method of claim 1, wherein the statistical quality indicator isweighted less during the calculation of the quality metric when thedetected ECG signal is of a higher quality.
 11. The method of claim 1,wherein the mid-beat SNR is weighted less during the calculation of thequality metric when the detected ECG signal is of a lower quality.